similar to: Error using glm with poisson family and identity link

Dear list, I'm trying to fit a glm model using family=poisson(link = "identity"). The problem is that the glm function fits a model with a negative intercept, which sounds like a nonsense to me, being the response a Poisson variable. >From a previous discussion on this list I've understood that the glm function uses IRLS for the fitting without any constraint so it is

Hi everyone, My background is not math and I am trying to figure out exactly what equation to use to map a response variable in GIS based on the coefficients obtained from the GLM and the values of the independent variables in each grid cell of my study area. Most specifically, I want to know how to incorporate the Poisson family and log link function in the equation. I would really appreciate if

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Dear R-helpers, I would like to compare the fit of two models, one of which I fit using lm() and the other using glm(family=poisson). The latter doesn't provide r-squared, so I wonder how to go about comparing these models (they have the same formula). Thanks very much, Mark Na [[alternative HTML version deleted]]

Hello! Anyone can help me to know how to do a bootstrap evaluation analysis to a GLM with family=Poisson? I have some R codes but they were coded only for family=binomial... thanks a lot Joana Vicente .?. CIBIO - Centro de Investiga??o em Biodiversidade e Recursos Gen?ticos. Faculdade de Ci?ncias do Porto Departamento de Bot?nica -Edif?cio FC4 Rua Do Campo Alegre, S/N 4169-007 Porto - Portugal

Dear R-users When running a glm polynomial model with one explanatory variable (example Y~X+X^2), with a poisson or binomial error distribution, the predicted values obtained from using the predict() function and those obtained from using the coefficients from the summary table "as is" in an equation of the form Y=INTERCEPT+ XCoef x X + XCoef x X^2, differ considerably. The former are

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear R/statistics wizards, This may be more of a statistics question than an R one, but I’m hoping that someone has the time to help. I’ve already consulted a few local statisticians and I’ve not yet received a clear answer. Before I start an experiment I want to conduct an a priori power analysis test for a Generalized Linear Model, family = poisson. The response variables will be counts.

Hello, I'm trying to run an lmer model with family poisson but receive the following warning message: model<-lmer(count~tem+dat+alt+year+tem:dat+tem:alt+tem:year+dat:alt+dat:year+alt:year+(1|id),family=poisson) Error in objective(.par, ...) : Leading minor of order 2 in downdated X'X is not positive definite I'm using the newest version of R and recently installed the

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Good afternoon, I REALLY try to answer to my question as an autonomous student searching in the huge pile of papers on my desk and on the Internet but I can't find out the solution. Would you mind giving me some help? Please. ######################################### I'm trying to use glm with factors: > Pyr.1.glm<-glm(Pyrale~Trait,DataRav,family=poisson) If I have correctly

Dear R-helpers, I'm using a GLM with poisson errors to model integer count data as a function of one non-integer covariate. The model formula is: log(DV) ~ glm(log(IV,10),family=poisson). I'm getting a warning because the logged DV is no longer an integer. I have three questions: 1) Can I ignore the warning, or is logging the DV (resulting in non-integers) a serious violation of the

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

Full_Name: saraux Version: 2.6.1 OS: Windows vista Submission from: (NULL) (193.157.180.37) I would like to compute a glm with a distribution of poisson, using a maximum of likelihood method. But it seems not to work with a distribution of poisson. The same code with another distrubution (binomial for example) works. Here is the command I typed:

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

Hi I'm a postgrad in ecology, and have recently started to use R. I'm planning to model various sets of animal abundance (i.e. count) data in relation to habitat data using glm's and/or glmmPQL's. However, some of my potential response variables have many zeros. From what I gather the "family = ..." option in the command line does not allow for the direct